Datastore Concepts Overview

The Datastore is a
schemaless NoSQL datastore providing robust, scalable storage for your
application, with the following features:

No planned downtime

Atomic transactions

High availability of reads and writes

Strong consistency for reads and ancestor queries

Eventual consistency for all other queries

The Datastore replicates data across multiple datacenters using a system based on the Paxos algorithm. This provides a high level of availability for reads and writes. Most queries are eventually consistent.

The Datastore holds data objects known as entities. An entity has one or more properties, named values of one of several supported data types: for instance, a property can be a string, an integer, or a reference to another entity. Each entity is identified by its kind, which
categorizes the entity for the purpose of queries, and a key that uniquely identifies it within its kind. The Datastore can execute multiple operations in a single transaction. By definition, a transaction cannot succeed unless every one of its operations succeeds; if any of the operations fails, the transaction is automatically rolled back.

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Comparison with traditional
databases

Unlike traditional relational databases, the Datastore uses a distributed architecture to automatically manage scaling to very large data sets. While the Datastore interface has many of the same features as traditional databases, it differs from them in the way it describes relationships between data objects. Entities of the same kind can have different properties, and different entities can have properties with the same name but different value types.

These unique characteristics imply a different way of designing and managing data to take advantage of the ability to scale automatically. In particular, the Datastore differs from a traditional relational database in the following important ways:

The Datastore is designed to scale, allowing applications to maintain high performance as they receive more traffic:

Datastore reads scale because the only queries supported are those whose performance scales with the size of the result set (as opposed to the data set). This means that a query whose result set contains 100 entities performs the same whether it searches over a hundred entities or a million. This property is the key reason some types of queries are not supported.

Because all queries are served by pre-built indexes, the types of queries that can be executed are more restrictive than those allowed on a relational database with SQL. In particular, the following are not supported:

Join operations

Inequality filtering on multiple properties

Filtering of data based on results of a subquery

Unlike traditional relational databases, the Datastore doesn't require entities of the same kind to have a consistent property set (although you can choose to enforce such a requirement in your own application code).

For more in-depth information about the design of the Datastore, read our series of articles on Mastering the Datastore.

Entities

Objects in the Datastore are known as entities. An entity has one or more named properties, each of which can have one or more values. Property values can belong to a variety of data types, including integers, floating-point numbers, strings, dates, and binary data, among others. A query on a property with multiple values tests whether any of the values meets the query criteria. This makes such properties useful for membership testing.

Note: Datastore entities are schemaless: unlike traditional relational databases, the Datastore does not require that all entities of a given kind have the same properties or that all of an entity's values for a given property be of the same data type. If a formal schema is needed, the application itself is responsible for ensuring that entities conform to it.

Kinds, keys, and identifiers

Each Datastore entity is of a particular kind, which categorizes the entity for the purpose of queries; for instance, a human resources application might represent each employee at a company with an entity of kind Employee. In addition, each entity has its own key, which uniquely identifies it. The key consists of the following components:

The entity's kind

An identifier, which can be either

a key name string

an integer ID

An optional ancestor path locating the entity within the Datastore hierarchy

The identifier is assigned when the entity is created. Because it is part of the entity's key, it is associated permanently with the entity and cannot be changed. It can be assigned in either of two ways:

Your application can specify its own key name string for the entity.

You can have the Datastore automatically assign the entity an integer numeric ID.

Ancestor paths

Entities in the Datastore form a hierarchically structured space similar to
the directory structure of a file system. When you create an entity, you can
optionally designate another entity as its parent; the new entity is a
child of the parent entity (note that unlike in a file system, the parent
entity need not actually exist). An entity without a parent is a root
entity. The association between an entity and its parent is permanent, and
cannot be changed once the entity is created. The Datastore will never assign
the same numeric ID to two entities with the same parent, or to two root
entities (those without a parent).

An entity's parent, parent's parent, and so on recursively, are its
ancestors; its children, children's children, and so on, are its
descendants. An entity and its descendants are said to belong to the
same entity group. The sequence of entities beginning with a root
entity and proceeding from parent to child, leading to a given entity,
constitute that entity's ancestor path. The complete key identifying
the entity consists of a sequence of kind-identifier pairs specifying its
ancestor path and terminating with those of the entity itself:

[Person:GreatGrandpa, Person:Grandpa, Person:Dad, Person:Me]

For a root entity, the ancestor path is empty and the key consists solely of
the entity's own kind and identifier:

[Person:GreatGrandpa]

Queries and indexes

In addition to retrieving entities from the Datastore directly by their
keys, an application can perform a
query
to retrieve them by the values of their properties.
The query operates on entities of a given
kind;
it can specify
filters
on the entities' property values, keys, and ancestors, and can return zero or
more entities as results. A query can also specify
sort orders
to sequence the results by their property values. The results include all
entities that have at least one
value for every property named in the filters and sort orders, and whose
property values meet all the specified filter criteria. The query can return
entire entities,
projected entities,
or just
entity keys.

When executed, the query retrieves all entities of the given kind that satisfy
all of the given filters, sorted in the specified order.

Note: To conserve memory and improve
performance, a query should, whenever possible, specify a limit on the number of
results returned.

A query can also include an ancestor filter limiting the results to just the entity group descended from a specified ancestor. Such a query is known as an ancestor query. By default, ancestor queries return strongly consistent results, which are guaranteed to be up to date with the latest changes to the
data. Non-ancestor queries, by contrast, can span the entire Datastore rather than just a single entity group, but are only eventually consistent and may return stale results. If strong consistency is important to your application, you may need to take this into account when structuring your data, placing related entities in the same entity group so they can be retrieved with an ancestor rather than a non-ancestor query; see Structuring Data for Strong Consistency for more information.

Every Datastore
query
computes its results using one or more
indexes,
tables containing entities in a sequence specified by the index's properties
and, optionally, the entity's ancestors. The indexes are updated incrementally
to reflect any changes the application makes to its entities, so that the
correct results of all queries are immediately available with no further
computation needed.

The Datastore
predefines a simple index on each property of an entity.
You
can define further custom indexes in an
index
configuration file named datastore-indexes.xml, which is generated in your application's /war/WEB-INF/appengine-generated directory. The development server automatically adds suggestions to this file as it encounters queries that cannot be executed with the existing indexes. You can tune indexes manually by editing the file before uploading the application.

Note:
The index-based query mechanism supports a wide range of queries and is suitable for
most applications. However, it does not support some kinds of query common in
other database technologies: in particular, joins and aggregate queries aren't
supported within the Datastore query engine. See
the
Datastore Queries page
for limitations on Datastore queries.

Transactions

Every attempt to insert, update, or delete an entity takes place in the context of a transaction. A single transaction can include any number of such operations. To maintain the consistency of the data, the transaction ensures that all of the operations it contains are applied to the Datastore as a unit or, if any of the operations fails, that none of them are applied.

You can perform multiple actions on an entity within a single transaction. For example, to increment a counter field in an object, you need to read the value of the counter, calculate the new value, and then store it back. Without a transaction, it is possible for another process to increment the counter between the time you read the value and the time you update it, causing your application to overwrite the updated value. Doing the read, calculation, and write in a single transaction ensures that no other process can interfere with the increment.

Transactions and entity groups

Only ancestor queries are allowed within a transaction: that is, each transactional query must be limited to a single entity group. The transaction itself can apply to multiple entities, but they must all be in a limited number (25) of entity groups.

The Datastore uses optimistic concurrency to manage transactions. When two or more transactions try to change the same entity group at the same time (either updating existing entities or creating new ones), the first transaction to commit will succeed and all others will fail on commit. These other transactions can then be retried on the updated data. Note that this limits the number of concurrent writes you can do to any entity in a given entity group.

Note: The first read of an entity group in a transaction may report an error if there is a conflict with other transactions accessing that same entity group. This means that even a transaction that performs only reads can fail with a concurrency exception.

Datastore writes and data visibility

Data is written to the Datastore in two phases:

In the Commit phase, the entity data is recorded in a log.

The Apply phase consists of two actions performed in parallel:

The entity data is written.

The index rows for the entity are written. (Note that this can take longer than writing the data itself.)

The write operation returns immediately after the Commit phase and the Apply phase then takes place asynchronously. If a failure occurs during the Commit phase, there are automatic retries; but if failures continue, the Datastore returns an error message that your application receives as an exception. If the Commit phase succeeds but the Apply fails, the Apply is rolled forward to completion when one of the following occurs:

Certain operations (lookup, commit, and ancestor queries) that use the affected entity group cause any changes that have been committed but not yet applied to be completed before proceeding with the new operation.

This write behavior can have several implications on how and when data is visible to your application at different parts of the Commit and Apply phases:

If a write operation reports a timeout error, it cannot be determined (without attempting to read the data) whether the operation succeeded or failed.

Because Datastore lookups and ancestor queries apply any outstanding modifications before executing, these operations always see a consistent view of all previous successful transactions. This means that a lookup operation (looking up an updated entity by its key) is guaranteed to see the latest version of that entity.

As long as a few hundred milliseconds may elapse from the time a write operation returns until the transaction is completely applied. In this case, queries spanning more than one entity group cannot determine whether there are any outstanding modifications before executing and may return stale results.

The timing of concurrent query requests may affect their results. If an entity initially satisfies a query but is later changed so that it no longer does, the entity may still be included in the query's result set; it will be omitted only if the query executes after the Apply phase of the update has been completed (that is, after the indexes have been written).